Medical characterization system

ABSTRACT

A medical characterization system is configured to input medical-related continuous parameters and discrete data so as to calculate a characterization timeline indicative of a physiological condition of a living being. A data source is in sensor communications with a patient so as to generate a continuous parameter. The data source also provides test data responsive to the patient at a test time. The test data is available to a characterization processor at a result time. The characterization processor is also responsive to the continuous parameter so as to generate a medical characterization as a function of time. A characterization analyzer enables the characterization processor to update the medical characterization in view of the test data as of the test time.

PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS

The present application claims priority benefit under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application Ser. No. 61/442,264 filed Feb.13, 2011, titled Complex System Characterizer hereby incorporated in itsentirety by reference herein.

SUMMARY OF THE INVENTION

A typical multi-parameter patient monitoring system (MPMS) derivesmultiple medical-related parameters and displays the results as variouscombinations of readouts and waveforms. A MPMS is responsive to sensorsattached to a patient and actively responds to the patient's physiology.Lacking, however, is the inclusion in patient monitoring of testmeasurements and other discrete data; previously recorded sensor data orparameters; and physiological data that has no particular time referencesuch as genetic information, family history and previous diagnoses, toname a few. Further, a MPMS does not provide a medical characterizationof a patient that includes a time element associated with testmeasurements and other discrete data including the time a test is takenor the time span of a parameter recording. Further, MPMS information isnot under dynamic user control so as to include or exclude informationto determine overall impact on a patient characterization.

One aspect of a medical characterization system is configured to inputmedical-related continuous parameters and discrete data so as tocalculate a characterization timeline indicative of a physiologicalcondition of a living being. The medical characterization system has aparameter generator, a characterization processor, a discrete datasource and a characterization analyzer. The parameter generator is insensor communications with a living being so as to generate a continuousparameter. The characterization processor is responsive to thecontinuous parameter so as to generate a medical characterization of theliving being as a function of time. The discrete data source provides adatum responsive to the living being at a first time and that isavailable to the characterization processor at a second time. Acharacterization analyzer enables the characterization processor toupdate the medical characterization in view of the datum as of the firsttime.

In various embodiments, the medical characterization system furthercomprises an analyzer model in communications with the characterizationanalyzer so as to determine the effect of the medical characterizationupdate over time. The analyzer model comprises a selectable one of anupward shift, a downward aging and an upward ramp. A data storage is incommunications with the data source and the characterization processorso that the characterization analyzer can selectively update pastportions of the medical characterization with later data. Aninput/output interface allows a person to selectively control themedical characterization updates. In an embodiment, the input/outputinterface has a display navigation tool that displays a selectable testepoch at the first time and a corresponding result epoch at the secondtime. In an embodiment, the analyzer model is responsive to one of atherapy time epoch and a test time epoch in view of a result epoch.

Another aspect of a medical characterization system are parametersgenerated in response to sensors in communication with a person. Amedical characterization is calculated from the parameters that isgenerally indicative of the physiological condition of the person. Amedical test is performed on the person at a test time. The medical testresult is received at a later result time. The medical characterizationis updated according to the medical test result as of the test time.

In various embodiments, the medical characterization models the behaviorof the medical characterization over time in response to the medicaltest as a test model. The medical characterization is displayed as afunction of time. The test time and the result time are indicated on thedisplay as a test and result epochs, respectively. At least one of thetest epoch and the result epoch are selected by a user so as to initiatethe updating. The test model is applied to the medical characterizationas of the test time in response to the selecting. A therapy time isindicated on the display as a therapy epoch. The behavior of the medicalcharacterization over time in response to a therapy is modeled astherapy effectiveness. A therapy time is indicated on the display as atherapy epoch. The therapy epoch is selected, and the therapyeffectiveness model is applied to the medical characterization as of thetherapy time in response.

A further aspect of a medical characterization system is an apparatuscomprising a data source, a characterization processor and acharacterization analyzer. The data source provides both a continuousparameter timeline and a discrete test result responsive to the medicalstate of a living being at a test time. The characterization processoris in communications with the data source so as to calculate a medicalcharacterization of the living being according to each of the continuousparameter and the discrete test result. The characterization analyzerupdates the continuous parameter timeline according to the discrete testresult as of the test time.

In various embodiments, the characterization processor, a processorengine and a processor model. The characterization processor has aninput selector that allows a user to select a current data input or async data input as a medical data output. The processor engine inputsthe medical data and generates a medical characterization. The processormodel determines how the medical characterization is calculated basedupon the medical data. The characterization analyzer has an analyzerengine that combines current medical data and recalled medical data togenerate sync data according to an analyzer model. A graphics generatoroutputs the medical characterization to a display. A marker generatorindicates test and result epochs on the display in conjunction with themedical characterization. An analyzer model determines the effect of atest result on the medical characterization. The analyzer model furtherindicates the effectiveness of an earlier therapy based upon the testresult.

Advantageously, a medical characterization system is configured to inputreal-time and non-real-time discrete and continuous medical-relatedparameters and data so as to calculate, in an embodiment, a risktimeline indicative of a probability of serious illness or death due toinjury, disease or other physiological conditions. The risk timeline isdynamically updated over past time segments as well as present time toaccount for newly received or previously unused parameters and data. Inan embodiment, the medical characterization system has a parametergenerator in sensor communications with a patient so as to generatecontinuous data streams indicative of the patient's physiologicalcondition. A risk processor responsive to the parameter generatorgenerates a risk timeline. A risk analyzer controls the risk processorso as to modify the risk timeline over past time segments as well aspresent time according to new information regarding the patient, such asmedical tests, diagnoses and therapies, to name a few. The risk analyzerrelates this new information back to the time that the informationoriginated. Further a medical characterization system advantageouslyallows a user to dynamically include or exclude individual parameters ordata or selected groups of parameters and data so as to determine theimpact on the risk timeline, both past and present.

Although an embodiment of a medical characterization system is describedwith respect to calculating and generating a dynamically adjustablemedical risk characterization timeline, in other embodiments a medicalcharacterization can reflect any of a variety of medicalcharacteristics, both general and specific, such as wellness, fitness orcompetitive readiness of athletes, to name a few. Further, although anembodiment of a medical characterization system is described withrespect to a single risk timeline, in other embodiments a medicalcharacterization system can calculate and simultaneously displaymultiple characteristics concurrently. For example, in addition to orinstead of an overall risk timeline, the characterization can bemultiple particularized risk timelines, such as an array of risks to aperson's circulatory, respiratory, neurological, gastrointestinal,urinary, immune, musculoskeletal, endocrine or reproductive systems.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a general block diagram of a medical characterization system;

FIGS. 2A-C are graphs of a medical characterization versus time, whichgenerally illustrate medical characterization;

FIG. 3 is detailed block diagram of a medical data source embodiment;

FIG. 4 is a detailed block diagram of a characterization processorembodiment;

FIG. 5 is a detailed block diagram of a characterization analyzerembodiment;

FIG. 6 is a detailed block diagram of an input/output (I/O) interfaceembodiment;

FIGS. 7A-D are graphs of exemplar analyzer models versus time;

FIGS. 8A-D are exemplar characterization versus time displaysillustrating a navigation tool for analyzing medical characterizations;

FIG. 9 is a detailed block diagram of a risk characterization systemembodiment;

FIG. 10 is a flow diagram of a risk processor embodiment;

FIG. 11 is a flow diagram of a subparameter risk calculator embodiment;

FIG. 12 is a flow diagram of a parameter risk calculator embodiment;

FIG. 13 is a block diagram of a risk analyzer embodiment; and

FIG. 14 is a block diagram of an I/O interface embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 generally illustrates a medical characterization system 100,which provides a medical characterization of a living being, such as apatient or person under medical care. The medical characterizationsystem 100 has data sources 110, a characterization processor 120, acharacterization analyzer 130, data storage 140 and I/O (input/output)150. Data sources 110 include various sensors and monitors incommunications 112 with a patient so as to generate parameters ortransmit data. Data sources 110 further include discrete data such astest results. As such, data sources 110 generally provide parameters,test data and other information 114 indicative of one or more aspects ofthe patient. The characterization processor 120 is responsive to thedata sources 110 so as to derive a medical characterization 122. In anembodiment, the medical characterization is a wellness or a risk index.The characterization processor 120 also generates epochs 124 indicatingdiscrete data, as described with respect to FIG. 4, below. Thecharacterization analyzer 130 advantageously updates and synchronizesthe characterization 122 so that it is accurate across all time periodsof interest. Also, the characterization analyzer 130 generates differentversions or realities of the medical characterization 122 based upon theinclusion or exclusion of available parameters and data 114. Thisadvantageously allows a care provider or other user to determine theimpact of that information 114 on the medical characterization 122.

FIGS. 2A-C generally illustrates functional aspects of acharacterization analyzer 130 (FIG. 1). A characterization processor 120(FIG. 1) generates an initial medical characterization 210 in responseto data sources 110 (FIG. 1), which is illustrated in a medicalcharacterization versus time graph 201 up to a present time 228. Thecharacterization analyzer 130 (FIG. 1) in conjunction with thecharacterization processor 120 (FIG. 1) also generates one or moreupdated characterizations 220 in response to a data source 110. Aparticular updated characterization 220 may provide an updated portion222 and retain a relatively instantaneous portion 224 of the initialcharacterization 210. In various embodiments, the updated portion 222may extend to present time 228 such that the instantaneous portion 224is negligible.

As shown in FIG. 2A, an initial medical characterization 210 may only beaccurate as of the current time 228. As such, it is difficult for a careprovider to accurately assess an individual's medical condition over aperiod of time based upon this information alone. In particular, thehistorical values 212 may well be out-of-date as more information aboutthe individual is received. This is particularly true if newly receivedinformation is not instantaneous, i.e. pertains to a past time. Thecharacterization analyzer 130 (FIG. 1) advantageously generates one ormore updates of the initial medical characterization 210 over some orall of the characterization time record so as to take into account notonly the patient history but also newly gained information that relatesback in time. The characterization analyzer 130 (FIG. 1) generates oneor more of these characterization updates 222 as the characterizationprocessor 120 (FIG. 1) continues to provide instantaneouscharacterizations 210. Further, a medical characterizer system 100(FIG. 1) embodiment has data storage 140 (FIG. 1) that advantageouslyrecords the initial characterization 210 and any or all subsequentupdated characterizations 220 for playback so that the impact of newlyreceived information may be reviewed and analyzed at thecharacterization processor output 122.

As shown in FIG. 2B, in a medical characterization versus time graph, aninitial medical characterization 230 is generated by thecharacterization processor 120 (FIG. 1). In an exemplar embodiment, themedical characterization 230 incorporates several parameters P1, P2 andP3. However, P1 has a processing time t1 245, P2 has a processing timet2 247 and P3 has a processing time t3 249. If these processing timesare not insignificantly short, the initial risk characterization 230 maynot be accurate. In particular, the parameters P1-P3 may not beapplicable to the present time 228, but rather relate back by theindividual computation times 245-249. The characterization analyzer 130(FIG. 1) advantageously calculates an updated characterization 242 thatrelates-back each parameter by its particular processing time 245-249.Accordingly, the updated characterization 240 has an updated portion 242and an instantaneous portion 244, as generally described with respect toFIG. 2A, above. The updated portion 242 advantageously takes intoaccount all parameter processing times 245-249. The instantaneousportion 244 may be ambiguous until the processing time issues areresolved.

As shown in FIG. 2C, an initial medical characterization 250 isgenerated by the characterization processor 120 (FIG. 1). At a specifictime 252, a patient test is initiated. This may be a blood test,urinalysis, x-rays or physical exam to name just a few. At some latertime 254, the characterization processor 120 (FIG. 1) receives the testresults. However, the test results are not applicable to the timereceived 254 but rather to the test time 252. The characterizationanalyzer 130 (FIG. 1) in conjunction with the characterization processor120 (FIG. 1) advantageously generate an updated medical characterization264 having a historical portion 262 and an updated portion 264. Thehistorical portion 262 remains unchanged from the initialcharacterization 250. The updated portion 264, however, advantageouslyrelates the test result 254 back to the test time 252. As such, theupdated characterization 264 provides a care provider an accuraterepresentation of an individual's medical status, such as risk orwellness, to name a few. Further, the care provider can advantageouslycompare the initial 250 and updated 260 medical characterizations todetermine the full impact over time of the test result 254 on theindividual's status.

FIG. 3 illustrates an exemplar medical data source 300 that is incommunications with a living being such as a patient 30 so as to outputinformation including parameters 301 and data 302 used to generate amedical characterization. One or more sensors 310 are in contact withthe subject 30 so as to generate continuous physiological information,such as information that is a continuous function of time over aparticular time segment and that regards the subject's physiologicalcondition. One or more monitors 330 may be in communications with thesensors 310 so as to calculate parameters 301. Parameters 301 aretypically realtime, continuous information generated from sensors 310and corresponding monitors 330, or sensors 310 alone, and areaccordingly immediately responsive (taking into account monitorprocessing times) to events occurring in realtime with the patient 30.Parameters 301 may also include segments of sensor 310 and monitor 330outputs which are recorded on a variety of analog and digital devicesincluding magnetic tape and disks, semiconductor memories and opticalstorage devices, to name a few examples, and played-back at a latertime. Sensors 310 may include optical sensors, such as pulse oximetrysensors; acoustic sensors, such as piezoelectric devices; blood pressuresensors, such as an inflatable cuff incorporating an audio transducer;airflow sensors and electrodes, to name just a few. Monitors 330 mayinclude pulse oximeters, advanced blood parameter monitors, acousticmonitors and capnography monitors, as examples. Recording devices mayinclude specialty devices, such as a Holter monitor for recording an ECGsignal, or any general data recording mechanism, such as semiconductormemory, magnetic disks, optical disks and the like.

Also shown in FIG. 3, one or more data sources 360 havingmedical-related information generate discrete information 302. Thediscrete information may be associated with a particular point in time(realtime data 370), or not associated with any particular point in time(non-realtime data 380). Realtime data 370 may include laboratory work,such as blood tests, urinalyses, X-rays or MRIs to name a few, whichgenerate results that can relate back to respective test times.Non-realtime information 380 is typically gathered from a variety ofsources and stored and accessed via one or more databases. Databases mayrange from a centralized database administered by a singleorganization/entity to a number of distributed and disparate databasesadministered by a variety of organizations/entities. Non-realtime data380 may include a subject's 30 medical history and pharmacological,genetic and environmental data, for example, which are not associatedwith any particular time or date or are too remote in time to relateback to any realtime parameters of interest.

FIG. 4 illustrates a characterization processor 400 embodiment having aninput selector 410, a processor engine 420 and processor models 430. Theinput selector 410 allows a care provider or other user to selectcurrent medical data 114 including parameters derived from sensors andmonitors and data derived from lab work and external databases. Theinput selector 410 also allows the care provider to select sync data 132generated by the analyzer engine 510 (FIG. 5), as described below. Inparticular, the input selector 410 may generate a medical data 412output to the processor engine 420 having current data 114 or sync data132, which is a combination of current and recalled data. In thismatter, a care provider may request a medical characterization 122 basedupon current information, such as a blood test result, related back tothe time the blood was drawn. This advantageously allows the blood testto be synchronized with parameters and other medical data at the timeblood was drawn as opposed to the potentially much later time when theblood was tested and the test results were made available. Thisback-in-time synchronization of new results with recalled data isdescribed in further detail with respect to FIGS. 7-8, below. Further, acare provider can generate multiple characterizations of variouscombinations of current data 114, including or excluding variousparameters or tests, say, so as to determine the effect on the medicalcharacterization 122.

Also shown in FIG. 4, processor models 430 determine what medicalcharacterization 122 is derived and how the derivation is calculated. Inan embodiment, the medical characterization 122 is a risk parameter,which advantageously provides a care provider with a real-time indexindicative of, in one embodiment, the physiologic deterioration in apatient. A risk characterization is described in further detail withrespect to FIGS. 9-14, below. Risk model embodiments for deriving a riskcharacterization are described with respect to FIGS. 11-12, below.

FIG. 5 illustrates a characterization analyzer 500 embodiment having ananalyzer engine 510 and analyzer models 520. The analyzer engine 510inputs current and recalled data 142 so as to generated a synchronized(sync) data 132 output. Sync data 132 represents the timesynchronization of current (new) data 114 (FIG. 1) with recalled (older)data 142 (FIG. 1), where, for example, the current data is used toupdate the recalled data so as to advantageously match a test resultreceived at a later time with medical data generated when the test wastaken. Such data synchronization is described in further detail withrespect to FIGS. 8A-D, below. The analyzer models 520 determine themanner in which current data is combined with recalled data. Variousanalyzer models are described with respect to FIGS. 7A-D, below.

FIG. 6 illustrates an input/output (I/O) 600 embodiment having agraphics generator 610, a display driver 620, a marker generator 630 anda control interface 640. Generally, the I/O 600 inputs characterizations122 and discrete data 124 from the characterization processor 122, whichare graphically displayed 622 via the display driver 620. In particular,the graphics generator 610 outputs a characterization curve 612interspersed with discrete variable epochs 632, as described below withrespect to FIGS. 8A-B. The display driver 620 generates a display output622 to any of various standard displays, such as a flat screen monitor,so as visually present the combined characterization curve 612 andepochs 632 to a care provider or other user. The care provider selectsone or more epochs 632 via controls 154, such as a keypad, mouse ortrackball, to name a few. The control interface generates user selects644 in response to the controls 154. The marker generator 630 isresponsive to the user selects 644 to mark the selected epochs 632,which also notifies the characterization analyzer accordingly.

FIGS. 7A-D illustrate exemplar characterization analyzer models. Asdescribed with respect to FIG. 5, above, a characterization analyzeradvantageously time synchronizes previously known medical informationwith updated medical information so that a characterization processormay accurately derive and display a medical characterization of apatient for evaluation by a care provider. An advantageous aspect ofcharacterization analysis is the accurate modeling, analysis and displayof a medical characterization so as to compensate for the time delaybetween a data measurement and a data result, as generally describedwith respect to FIGS. 2A-C, above.

FIG. 7A graphs a parameter 711, a shift model 712, a medicalcharacterization 713 and an adjusted medical characterization 714. Theparameter graph 711 depicts a parameter measurement 730 at a test time721 yielding a result at a result time 722. The model graph 712 depictsa medical characterization modeled as a step change or shift in thecharacterization 723 at the test time 721. The characterization graph713 depicts a medical characterization 724 based upon the parameter 711before the result 722 is known. The adjusted characterization graph 714depicts the medical characterization before 724 and after 725 themodeled shift 723. For example, the parameter 711 may be Hb. The testmay be a blood draw indicating an abnormally low hemoglobin. Thecharacterization 713 and adjusted characterization 714 may be medicalrisk (see FIGS. 9-14, below), so as to indicate a step change in risk724, 725 at the time of the test 721 as compared with the time of thetest result 722.

FIG. 7B graphs a parameter 731, an aging model 732, a medicalcharacterization 733 and an adjusted medical characterization 734. Theparameter graph 731 depicts a parameter measurement 740 at a test time741 yielding a result at a result time 742. The model graph 732 depictsa medical characterization modeled as an aging, i.e. a step change orshift in the characterization 743 at the test time 741 followed by adecreasing change 744 over time. The characterization graph 733 depictsa medical characterization 745 based upon the parameter 731 before theresult 742 is known. The adjusted characterization graph 734 depicts themedical characterization before 746 and after 747 the modeled aging 743,744. For example, the parameter 731 may be body tem peratue. The testmay be a white blood cell count blood draw indicating a possibleinfection. The characterization 733 and adjusted characterization 734may be medical risk so as to indicate an initial increase in risk 746,747 at the time of the test 741, which diminishes over time as theresult of known treatments or the fact that the test result becomes oldand increasingly unreliable over time.

FIG. 7C graphs a parameter 751, a ramp model 752, a medicalcharacterization 753 and an adjusted medical characterization 754. Theparameter graph 751 depicts parameter measurements 760, 761 at a testtimes 762, 763 yielding results at result times 764, 765. The modelgraph 752 depicts a medical characterization modeled as a ramp-up 766,i.e. an increasing characterization at test time 762 that levels off 767at test time 763 or continues to increase 768. The characterizationgraph 753 depicts a medical characterization 770 based upon theparameter 751 before the results 764, 765 are known. The adjustedcharacterization graph 754 depicts the medical characterization before771 and after 772 the modeled ramp 766, 767. For example, the parameter751 may be blood pressure. The test may be blood draws indicating Hctlevels are decreasing over time. The characterization 753 and adjustedcharacterization 754 may be medical risk so as to indicate an increasingrisk 771, 772 over time 762, 763.

FIG. 7D graphs a parameter 781, an effectivity model 782, a medicalcharacterization 783 and an adjusted medical characterization 784. Theparameter graph 781 depicts therapy 792 applied at a time 791 and afollow-up test 794 at a time 793 with a result at time 795. A firstmodel graph 782 depicts a medical characterization modeled as ancharacterization decrease 796 over time, which depicts an effectivetherapy based upon the test results 795. A second model graph 783depicts a medical characterization modeled as an characterizationincrease 797 over time, which depicts an ineffective therapy based uponthe test results 794. The characterization graph 784 depicts a medicalcharacterization 790 based upon the parameter 781 before the result 795is known. The adjusted characterization graphs 798, 799 depict themedical characterization applied at the time of the therapy 791,assuming an effective 798 or ineffective 799 therapy, respectively. Forexample, the therapy may be administration of antibiotic and the testmay be throat culture. Advantageously, the various models allow amedical characterization, such as risk, to be accurately reflected as ofthe time of a test or as of the time of an applied therapy, as examples.Further, the models advantageously allow the medical characterization tobe modeled back in time in a variety of ways depending on the parametermeasured, the type of test, the number of tests and therapies applied.These models may variously reflect characterization shifts, aging, rampsas examples. In other embodiments, multiple tests may allow acharacterization model to be a parametric curve depending on the testtimes and results.

FIG. 8A-B illustrates a display navigation tool (DVT) 800 that user I/O150 (FIG. 1) generates on a user display 152 (FIG. 1). The DVTadvantageously allows a care provider or other user to selectivelycontrol the incorporation of test data into a medical characterizationof a patient or other living being. In particular, a characterizationprocessor 120 (FIG. 1) generates a characterization 122 output, which isviewed as a characterization 801 timeline on the user display.Advantageously, user I/O superimposes discrete test epochs 812-816 andcorresponding result epochs 822-826 on the characterization 801 timelineso that a user can selectively incorporate discrete test data intocharacterization calculations. In an embodiment, the characterization isa measure of risk.

As shown in FIG. 8A, in a particularly advantageous embodiment, test andresult epochs 805, 806 are displayed as paired flags. A first set offlags 805 indicate tests and a second set of flags indicate results. Inan embodiment, the test flags 805 extend above the characterization 801timeline and result flags 806 extend below the characterization 801timeline. In this manner, a user viewing the display can readilydetermine the time a test occurs and the time a corresponding result isreceived. In an embodiment, test/result pairs (e.g. 812/822; 814/824 and816/826) are shown with unique matching flags so that a user viewing thedisplay can readily determine matching pairs and distinguish them fromother matching pairs.

As shown in FIG. 8B, a selected test flag 814 and corresponding testresult flag 824 are indicated on the display by bolding, coloring orotherwise highlighting the flag pair 814/824. Once a particular pair isselected, a user can initiate a characterization recalculation, asdescribed with respect to FIGS. 1-7, above. The characterizationrecalculation modifies the characterization timeline to account for thetest data, and this modification relates back to the test epoch. Acorresponding characterization recharacterization 802 timeline isdisplayed, where the test result 824 at time t_(f) relates back to thetest time t_(i). The characterization recharacterization generates anupdated characterization 851 timeline portion and a historical(unchanged) characterization 850 timeline portion.

In an embodiment, a user temporarily positions a cursor (via a mouse orother pointing device) over a test or result so as to trigger a pop-upthat provides a written description of the test or result. Thedescription may indicate the kind of test (e.g. blood analysis, x-ray,urinalysis, etc.); the time and date of the test and result; the testsource, such as a specific laboratory; and the physician in charge, toname just a few. Multiple test/result pairs may be selected so as toallow a user to see the impact on the characterization of multiplegroups of tests. In other embodiments, not shown, non-realtime dataincluding personal history (e.g. smoking, alcohol or drug abuse);medical history (e.g. cancer, heart disease, congenital defects); familyhistory; personal genome data, among many others, are listed on demandand selectable individually or in groups so as to recharacterize riskaccordingly.

As shown in FIG. 8C, in a particularly advantageous embodiment, multiplerelated test and result epochs 824-838 are displayed with matching flagsand can be simultaneously selected so as to trigger multi-pointcharacterization models relating back to multiple tests 824, 826. Asshown in FIG. 8D, a selected set of therapy and test flags 832-836 andcorresponding result flags 836, 838 are indicated on the display bybolding, coloring or otherwise highlighting the flag sets 832/834/836.Once a particular set is selected, a user can initiate acharacterization recalculation, as described with respect to FIGS. 7A-D,above. The characterization recalculation modifies the characterizationtimeline to account for the therapy and/or test data, and thismodification relates back to a therapy or test epoch. A correspondingrecharacterization 804 timeline is displayed, where the test result 836at time t₄ relates back to a test time t₂, a therapy time t₁ or acombination of test or therapy times.

FIG. 9 illustrates a medical risk system 900 embodiment of a medicalcharacterization system. Generally, the medical risk system 900characterizes a person with respect to their physiological wellness orillness. In an embodiment, the medical risk system 900 advantageouslyindicates a potential for near-term serious physiological impairment ordeath due any one or more of disease, injury, surgical complications,drug side-effects or allergic reactions, to name just a few.

As shown in FIG. 9, medical risk system 900 has a parameter generator910, a risk processor 1000, user input/output 920, data storage 930 anda risk analyzer 1000 all communicating over a common network 5.Generally, the parameter generator 910 is in communications with apatient 30 and various information sources 40 regarding the patient soas to generate parameters and data 912 (collectively “medical data”)indicative of a patient's medical state. This data 912 is stored as oneor more records 934 in the data storage 930. The risk processor 1000 isresponsive to the data 934 so as to generate a risk 1001 output,indicative of the patient's medical risk. Risk 1001 is stored as one ormore risk records 936 in the data storage 930. In an embodiment, risk1001 is a function of time having a high value if a person is at a highrisk of an impeding serious or life-threatening physiological event anda low value if a person has a correspondingly low risk of such an event.In an embodiment, the risk processor 1000 functions in conjunction withthe risk analyzer 1300 to update risk records 936 with new data 912 soas to generate additional risk records 936, as described with respect toFIGS. 10-14, below. User I/O 920 allows doctors, medical staff,researchers and other care providers 50 to review and accurately modifyrisk records 936, to assess the impact on medical risk of newly obtainpatient data 912 and to control the functions of the risk processor 1000and risk analyzer 1300.

A wellness analysis system that integrates real-time sensor data from apatient or other subject regarding the status of any or all of asubject's circulatory, respiratory, neurological, gastrointestinal,urinary, immune, musculoskeletal, endocrine and reproductive systems andnon-real-time information regarding the subject such as a lab work,pharmaceuticals and medications, medical history, genetics andenvironment from hospital records and other databases so as to generatea current or predictive wellness index or related output is described inU.S. patent application Ser. No. 13/009,505, filed Jan. 19, 2011, titledWellness Analysis System, assigned to Masimo Corporation, IrvineCorporation (“Masimo”) and hereby incorporated by reference herein. Arisk analysis system that inputs sensor data from a subject, derivescorresponding physiological parameters, assesses parameter risksaccording to parameter values and the impact those values have on thesubject's physiology and estimates a total risk from a combination ofthe parameter risks, where total risk is a numerical indication of thelikelihood of serious illness or debilitation or, in contrast, thelikelihood of wellness or health is described in U.S. patent applicationSer. No. 13/269,296, filed Oct. 7, 2011, titled Risk Analysis System,assigned to Masimo and hereby incorporated by reference herein.

FIG. 10 illustrates a medical risk processor 1000 embodiment havinginput parameters 1001 and generating a risk 1003 output. The riskprocessor 1000 has a parameter risk calculator 1010, a total riskcalculator 1020 and a risk calculation controller 1030. The parameterrisk calculator 1010 inputs parameters 1001 and generates correspondingparameter risks 1011. The total risk calculator 1020 inputs theparameter risks 1011 and generates the risk 1003 output. The riskcalculation controller 1030 advantageously modifies the parameter risks1101 and the risk 1003 in response to controls 1002 from the riskanalyzer 1000 (FIG. 10). Controls 1024 allow the risk analyzer 1303(FIG. 13) to dynamically modify risk 701 in response to changes in themonitored parameters, new test results or data updates pertaining to thesubject monitored.

As shown in FIG. 10, controls 1002 may indicate which parameters 1001are active, what discrete real-time data, such as lab work, is availableand what non-real-time data, such as medical history, is available. Therisk calculation controller 1030 responds to the controls 1002 so as togenerate sub-parameter adjusts 1032 to the parameter risk calculation1010, as described with respect to FIG. 11, below. The risk calculationcontroller 1030 also responds to the controls 1002 so as to generaterisk adjusts 1034 to the total risk calculation 1020, as described withrespect to FIG. 12, below.

FIG. 11 illustrates a parameter risk calculator 1100 having inputparameters PARA₁-PARA_(N) 1101 and output parameter risks PARA₁RISK-PARA_(N) RISK 1103. Each of the parameter risks 1103 is calculatedindependent of the others. Detailed in FIG. 11 is a risk calculation forPARA_(M) 1105, which yields PARA_(M) RISK 1180. Initially, sub-parametercalculators 1110 factor PARA_(M) 1105 into a corresponding set ofsub-parameters SUBP₁-SUBP_(n) 1120. In particular, the sub-parameterscalculators 1110 are each responsive to a particular feature of theparameter 1105. Generally, these features are chosen so that thecorresponding sub-parameter risks SUBP₁ RISK-SUBP_(n) RISK 1140, as aset, are representative of the risk associated with the particular inputparameter PARA_(M) 1105. For example, an oxygen saturation parametermight be factored into the sub-parameters saturation baseline,saturation instability and saturation average slope.

As shown in FIG. 11, sub-parameter risk calculators 1130 derivesub-parameters risks 1140 from the sub-parameters 1120. A sub-parameterrisk calculator 1130 is a risk versus parameter value function(illustrated graphically herein). Accordingly, each sub-parameter riskcalculator 1130 converts sub-parameter 1120 values into risks rangingbetween 0 to 1 (0% to 100% risk) according to the physiologicalcharacteristic represented by that sub-parameter 1120. For example,according to a risk function 1132, SUBP₁ 1122 has a maximum risk of 1for a range of low values, and this risk decreases in inverse proportionSUBP₁ as SUBP₁ 1122 increases, eventually approaching 0 risk at thehighest SUBP₁ values.

Further shown in FIG. 11, the sub-parameter risks 1140 are then weighted1150 to yield weighted sub-parameter risks 1160, which are summed 1170to yield the parameter risk PARA_(M) RISK 1180. In an embodiment, thesub-parameter risk weights 1150 add to a value of 1. Accordingly, theweighted sub-parameter risks 1160 sum to a maximum value of 1, andPARA_(M) RISK 1180 also varies between 0 to 1 (0% to 100% risk).

Additionally shown in FIG. 11, the sub-parameter risk calculators 1130and the sub-parameter risk weights 1150 are dynamically adjustable bySUBP ADJUST controls 1102, which are responsive to controls thatoriginate from the risk analyzer 1300 (FIG. 13). Accordingly, SUBPADJUST 1102 advantageously responds to discrete realtime data, such astest results, and non-realtime data, such as known disease conditions,family history, genetics and the like. Accordingly, the relative weights850 for one or more sub-parameter risks 840 are also responsive to SUBPADJUST controls 722.

FIG. 12 illustrates a total risk calculation 1200 having parameter risk1201 inputs and generating a risk 1203 output. In an embodiment, theparameter risks 1201 are assigned parameter risk weights 1210 so thatthe risk 1201 ranges between 0 to 1. Some parameter risks 1201 areassigned a higher weight to reflect a higher relative contribution ofthose parameters risks 1201 to the (total) risk 1203 output.

As shown in FIG. 12, the parameter weights 1210 are advantageouslyadjustable by RISK ADJUST 1202, which originates from the riskcalculation controller 1030 (FIG. 10) and is responsive to controls 1303(FIG. 13) from the risk analyzer 1300 (FIG. 13). For example, a user canadvantageously determine the impact an individual parameter has on risk1203 over any given time span by utilizing the risk analyzer 1300 (FIG.13) to assign a zero weight 1210 to that parameter and adjusting otherweights 1210 accordingly via controls 1303 (FIG. 13) and RISK ADJUST1202. As another example, weights 1210 can be adjusted to reflect newlyreceived test data or historical or background information, whichindicate that relative parameter risks have changed. As a furtherexample, weights 1210 can be adjusted as described above to reflect arecorded parameter that is active only for a specified time period. Asyet another example, some weights 1210 can be zeroed and other weights1201 adjusted accordingly if one or more parameters are disconnected orotherwise become inactive.

FIG. 13 illustrates a risk analyzer 1300 that functions in conjunctionwith a risk processor 1000 (FIG. 10) to re-characterize a medical riskcalculation. Risk re-characterization may involve incorporating new orpreviously unused data into a risk calculation. Such data includesrealtime discrete data, such as a lab test that generates a laterresult; non-realtime discrete data, such as a datum of medical history;and a time segment of previously recorded parameter data, to name a few.Risk re-characterization may also involve recalculating risk excludingone or more previously included parameters so as to allow a user todetermine the impact on risk of those parameters.

As shown in FIG. 13, the risk analyzer 1300 has a record requester 1310and a risk processor controller 1320. The record requester 1310 isresponsive to a data ID 1301 input so as to generate a record ID 1302output to the data storage 930 (FIG. 9). The data ID 1301 originatesfrom user I/O 920 (FIG. 9) according to a user selection of previouslyexcluded data, as described with respect to FIG. 9, below. Inparticular, the record ID 1302 specifies one or more data records 932(FIG. 9) to retrieve from the data storage 930 (FIG. 9) for the riskprocessor 1000 (FIG. 9). These records may include parameters and datapreviously used to calculate risk along with records of heretoforeunused parameters and data that the user 50 (FIG. 9) has currentlyselected, collectively “active” data.

Also shown in FIG. 13, the record requester 1310 communicates this“active” data 1312 to the risk processor controller 1320, whichgenerates controls 1303 to the risk processor 1000 (FIG. 9). Thesecontrols 1303 include PARA 1001, RT 1003 and NT 1005 outputs forsignaling the risk processor 1000 (FIG. 9) which parameters and discretedata to include in the current calculation of risk. In particular, PARA1001 specifies which input parameters 912 (FIG. 9) are active. RT 1003specifies discrete real-time data and NT 1005 specifies non-real-timedata to be used in the risk calculations. The risk calculationcontroller 1030 (FIG. 10) responds to PARA 1001 to generate risk adjust1034 (FIG. 10), which causes the risk calculation 1200 (FIG. 12) toignore inactive parameters, as described above. The risk calculationcontroller 1030 (FIG. 10) also responds to RT 1003 to generatesub-parameter adjust 1032 (FIG. 10), which causes the parameter riskcalculator 1000 (FIG. 10) to modify sub-parameter risks 1140 (FIG. 11)to account for real-time discrete data. The risk calculation controller1030 (FIG. 10) further responds to NRT 1005 to generate sub-parameteradjust 1032 (FIG. 10), which causes the parameter risk calculator 1010(FIG. 10) to factor in particular subject data, as described above.

FIG. 14 illustrates a user input/output (I/O) 1400 that provides userdisplay and control for risk characterization and recharacterization ofinput parameters and data. The user I/O 1400 has a marker generator 1410and a user interface 1420. The marker generator 1410 advantageouslyflags test and result epochs on a user display, as illustrated anddescribed with respect to FIGS. 8A-D, below. In particular, the markergenerator 1410 has a parameter record input 1401 from the data store andgenerates flags 1403 to a display 940 (FIG. 9). The flags 1403 areadvantageously used to identify the occurrence of test data and laterresults relative to a risk record. A user interface 1420 is responsiveto user selections 1402 from a user input to select 1402 one or more ofthese epochs, which may cause the marker generator 1410 to highlight aparticular flag or otherwise indicate its selection the display.Further, the user selection 1402 generates a data ID 1404 to the riskanalyzer 1300 (FIG. 13), which generates a record ID 1302 (FIG. 13) andcontrols 1303 (FIG. 13) so as to access the selected data from the datastorage and process the data in the risk processor 1000 (FIG. 10)accordingly.

A medical characterization system has been disclosed in detail inconnection with various embodiments. These embodiments are disclosed byway of examples only and are not to limit the scope of the claims thatfollow. One of ordinary skill in the art will appreciate many variationsand modifications.

What is claimed is:
 1. A method of treating a patient, the methodcomprising: receiving optical data from an optical sensor; determining aplurality of parameters based on the received optical data, wherein atleast one of the plurality of parameters comprise oxygen saturation;extracting a plurality of subparameters corresponding to the oxygensaturation, wherein the subparameters comprise saturation baseline,saturation instability, and saturation average slope; applying arespective risk function to each of the plurality of subparameters;calculating a plurality of subparameter risk values based on theapplication of respective risk functions; calculating an oxygensaturation risk based on an application of a plurality of weights to thecalculated plurality of subparameter risk values; generating a healthindex based on the calculated oxygen saturation risk and risksassociated with rest of the plurality of parameters; displaying thehealth index on a display; and treating the patient based on the healthindex.
 2. The method of claim 1, further comprising allowing a user todynamically include or exclude the plurality of parameters fromcalculation of the health index and updating the health index based onthe user selection.
 3. The method of claim 1, further comprisingmodifying the health index based on non-real-time data.
 4. The method ofclaim 1, wherein the plurality of weights are automatically selectedbased on active data.
 5. The method of claim 1, wherein the plurality ofweights are automatically selected based on user defined criteria. 6.The method of claim 1, wherein the displaying further comprisesdisplaying a historical trend of the health index.
 7. The method ofclaim 1, wherein the displaying further comprises displaying a pluralityof markers.
 8. The method of claim 1, wherein the plurality ofparameters comprise real-time and non-real-time parameters.
 9. A systemfor treating a patient, the system comprising one or more hardwareprocessors configured to: receive optical data from an optical sensor;determine a plurality of parameters based on the received optical data,wherein at least one of the plurality of parameters comprise oxygensaturation; extract a plurality of subparameters corresponding to theoxygen saturation; apply a respective risk function to each of theplurality of subparameters; calculate a plurality of subparameter riskvalues based on the application of respective risk functions; calculatean oxygen saturation risk based on an application of a plurality ofweights to the calculated plurality of subparameter risk values;generate a health index based on the calculated oxygen saturation riskand risks associated with rest of the plurality of parameters; anddisplay the health index, wherein the health index is configured toprovide a caregiver information for treating the patient.
 10. The systemof claim 9, wherein the one or more hardware processors are furtherconfigured to allow a user to dynamically include or exclude theplurality of parameters from calculation of the health index and updatethe health index based on the user selection.
 11. The system of claim 9,wherein the one or more hardware processors are further configured tomodify the health index based on non-real-time data.
 12. The system ofclaim 9, wherein the plurality of weights are automatically selectedbased on active data.
 13. The system of claim 9, wherein the pluralityof weights are automatically selected based on user defined criteria.14. The system of claim 9, wherein the one or more hardware processorsare further configured to display a historical trend of the healthindex.
 15. The system of claim 9, wherein the one or more hardwareprocessors are further configured to display a plurality of markers. 16.The system of claim 9, wherein the subparameters comprise saturationbaseline, saturation instability, and saturation average slope.